Power analysis for IoT devices is strongly required to protect attacks from malicious attackers. It is also very important to reduce power consumption itself of IoT devices. In this paper, we propose a highly-adaptable and small-sized in-field power analyzer for low-power IoT devices. The proposed power analyzer has the following advantages: (A) The proposed power analyzer realizes signal-averaging noise reduction with synchronization signal lines and thus it can reduce wide frequency range of noises; (B) The proposed power analyzer partitions a long-term power analysis process into several analysis segments and measures voltages and currents of each analysis segment by using small amount of data memories. By combining these analysis segments, we can obtain long-term analysis results; (C) The proposed power analyzer has two amplifiers that amplify current signals adaptively depending on their magnitude. Hence maximum readable current can be increased with keeping minimum readable current small enough. Since all of (A), (B) and (C) do not require complicated mechanisms nor circuits, the proposed power analyzer is implemented on just a 2.5cm×3.3cm board, which is the smallest size among the other existing power analyzers for IoT devices. We have measured power and energy consumption of the AES encryption process on the IoT device and demonstrated that the proposed power analyzer has only up to 1.17% measurement errors compared to a high-precision oscilloscope.

This paper proposes a deep quasi-linear kernel for support vector machines (SVMs). The deep quasi-linear kernel can be constructed by using a pre-trained deep neural network. To realize this goal, a multilayer gated bilinear classifier is first designed to mimic the functionality of the pre-trained deep neural network, by generating the gate control signals using the deep neural network. Then, a deep quasi-linear kernel is derived by applying an SVM formulation to the multilayer gated bilinear classifier. In this way, we are able to further implicitly optimize the parameters of the multilayer gated bilinear classifier, which are a set of duplicate but independent parameters of the pre-trained deep neural network, by using an SVM optimization. Experimental results on different data sets show that SVMs with the proposed deep quasi-linear kernel have an ability to take advantage of the pre-trained deep neural networks and outperform SVMs with RBF kernels.

In this letter, we show the new signal power estimation method base on the subspace projection. This work mainly contributes to the SINR estimation problem because, in this research, the signal power estimation is implicitly or explicitly performed. The difference between our method and the conventional method related to this topic is the exploitation of the subspace character of the signals constructing the observed signal. As tools to perform subspace operation, we apply orthogonal projection and oblique projection which can extracts desired parameters. In the proposed scheme, the statistics of the projected observed signal by these projection are used to estimate the parameters.

Return-oriented programming (ROP) attacks, which have been increasing in number recently, are an exploitation technique that can bypass non-executable page protection methods by using codes that exist within benign programs or modules. There have been many studies on defense against ROP attacks, but most of them have high overhead or high time complexity in terms of the detection of gadgets. In this letter, we suggest an ROP defense technique which is fast, space-efficient, and of lower detection time complexity; it uses a compiler-based approach. The most recent ROP defense technique is a compiler-based zero-sum defender suggested by Kim et al., achieving very low overhead. However, it still did not solve the issue of time complexity regarding detection. Our technique performs a specific computation to identify gadgets at the resetting position immediately before and after a return instruction. This method can efficiently identify a series of gadgets performed without calls and defend against them. In our experiment, the performance overhead was 1.62% and the file size overhead was 4.60%; our proposed technique achieved O(1) in terms of time complexity while having almost the same overhead as the zero-sum defender.

In this paper, we propose a novel placement algorithm for mixed-grained reconfigurable architectures (MGRAs). MGRA consists of coarse-grained and fine-grained clusters, in order to implement a combined digital systems of high-speed data paths with multi-bit operands and random logic circuits for state machines and bit-wise operations. For accelerating simulated annealing based FPGA placement algorithm, range limiter has been proposed to control the distance of two blocks to be interchanged. However, it is not applicable to MGRAs due to the heterogeneous structure of MGRAs. Proposed range limiter using connection bounding box effectively keeps the size of range limiter to encourage moves across fine-grain blocks in non-adjacent clusters. From experimental results, the proposed method achieved 47.8% reduction of cost in the best case compared with conventional methods.

Average coding rate of a multi-shot Tunstall code, which is a variation of variable-to-fixed length (VF) lossless source codes, for stationary memoryless sources is investigated. A multi-shot VF code parses a given source sequence to variable-length blocks and encodes them to fixed-length codewords. If we consider the situation that the parsing count is fixed, overall multi-shot VF code can be treated as a one-shot VF code. For this setting of Tunstall code, the compression performance is evaluated using two criterions. The first one is the average coding rate which is defined as the codeword length divided by the average block length. The second one is the expectation of the pointwise coding rate. It is proved that both of the above average coding rate converge to the entropy of a stationary memoryless source under the assumption that the geometric mean of the leaf counts of the multi-shot Tunstall parsing trees goes to infinity.

Many studies have applied the three-dimensional discrete wavelet transform (3D DWT) to video coding. It is known that corruptions of the lowest frequency sub-band (LL) coefficients of 3D DWT severely affect the visual quality of video. Recently, we proposed an error resilient 3D DWT video coding method (the conventional method) that employs dispersive grouping and an error concealment (EC). The EC scheme of our conventional method adopts a replacement technique of the lost LL coefficients. In this paper, we propose a new 3D DWT video transmission method in order to enhance error resilience. The proposed method adopts an error correction scheme using invertible codes to protect LL coefficients. We use half-rate Reed-Solomon (RS) codes as invertible codes. Additionally, to improve performance by using the effect of interleave, we adopt a new configuration scheme at the RS encoding stage. The evaluation by computer simulation compares the performance of the proposed method with that of other EC methods, and indicates the advantage of the proposed method.

In this paper a hardware-efficient local extrema detection (LED) method used for scale-space extrema detection in the SIFT algorithm is proposed. By reformulating the reuse of the intermediate results in taking the local maximum and minimum, the necessary operations in LED are reduced without degrading the detection accuracy. The proposed method requires 25% to 35% less logic resources than the conventional method when implemented in an FPGA with a slight increase in latency.

Let G be a graph and K be a set of target vertices of G. Assume that all vertices of G, except the vertices in K, may fail with given probabilities. The K-terminal reliability of G is the probability that all vertices in K are mutually connected. This reliability problem is known to be #P-complete for general graphs. This work develops the first polynomial-time algorithm for computing the K-terminal reliability of circular-arc graphs.

A generalization of compression via substring enumeration (CSE) for k-th order Markov sources with a finite alphabet is proposed, and an upper bound of the codeword length of the proposed method is presented. We analyze the worst case maximum redundancy of CSE for k-th order Markov sources with a finite alphabet. The compression ratio of the proposed method asymptotically converges to the optimal one for k-th order Markov sources with a finite alphabet if the length n of a source string tends to infinity.

The design and performance evaluation is presented of a partially adaptive array that suppresses clutter from low elevation angles in atmospheric radar observations. The norm-constrained and directionally constrained minimization of power (NC-DCMP) algorithm has been widely used to suppress clutter in atmospheric radars, because it can limit the signal-to-noise ratio (SNR) loss to a designated amount, which is the most important design factor for atmospheric radars. To suppress clutter from low elevation angles, adding supplemental antennas that have high response to the incoming directions of clutter has been considered to be more efficient than to divide uniformly the high-gain main array. However, the proper handling of the gain differences of main and sub-arrays has not been well studied. We performed numerical simulations to show that using the proper gain weighting, the sub-array configuration has better clutter suppression capability per unit SNR loss than the uniformly divided arrays of the same size. The method developed is also applied to an actual observation dataset from the MU radar at Shigaraki, Japan. The properly gain-weighted NC-DCMP algorithm suppresses the ground clutter sufficiently with an average SNR loss of about 1 dB less than that of the uniform-gain configuration.

The main contribution of this paper is a non-trivial expression, that is called dual expression, of the posterior values for non-adaptive group testing problems. The dual expression is useful for exact bitwise MAP estimation. We assume a simplest non-adaptive group testing scenario including N-objects with binary status and M-tests. If a group contains one or more positive object, the test result for the group is assumed to be one; otherwise, the test result becomes zero. Our inference problem is to evaluate the posterior probabilities of the objects from the observation of M-test results and the prior probabilities for objects. The derivation of the dual expression of posterior values can be naturally described based on a holographic transformation to the normal factor graph (NFG) representing the inference problem. In order to handle OR constraints in the NFG, we introduce a novel holographic transformation that converts an OR function to a function similar to an EQUAL function.

This letter investigates transmit optimization in multi-user multi-input multi-output (MIMO) wiretap channels. In particular, we address the transmit covariance optimization for an artificial-noise (AN)-aided secrecy rate maximization (SRM) when subject to individual harvested energy and average transmit power. Owing to the inefficiency of the conventional interior-point solvers in handling our formulated SRM problem, a custom-designed algorithm based on penalty function (PF) and projected gradient (PG) is proposed, which results in semi-closed form solutions. The proposed algorithm achieves about two orders of magnitude reduction of running time with nearly the same performance comparing to the existing interior-point solvers. In addition, the proposed algorithm can be extended to other power-limited transmit design problems. Simulation results demonstrate the excellent performance and high efficiency of the algorithm.

We propose a synthesis and automatic layout method for mixed-signal circuits with high regularity. As the first step of this research, a resistive digital-to-analog converter (RDAC) is presented. With a size calculation routine, the area of this RDAC is minimized while satisfying the required matching precision without any optimization loops. We propose to partition the design into slices comprising of both analog and digital cells. These cells are programmed to be synthesized as similar as custom P-Cells based on the calculation above, and automatically laid out to form one slice cell. To synthesize digital circuits, without using digital standard cell library, we propose a versatile unit digital block consisting of 8 transistors. With one or several blocks, the transistors' interconnections are programmed in the units to realize various logic gates. By using this block, the slice shapes are aligned so that the layout space in between the slices are minimized. The proposed mixed-signal slice-based partition facilitates the place-and-route of the whole RDAC. The post-layout simulation shows that the generated 9-bit RDAC achieves 1GHz sampling frequency, -0.11/0.09 and -0.30/0.75 DNL and INL, respectively, 3.57mW power consumption, and 0.0038mm2 active area.

In this paper, we identify the tradeoff between security and reliability in the amplify-and-forward (AF) distributed beamforming (DBF) cooperative network with K untrusted relays. In particular, we derive the closed-form expressions for the connection outage probability (COP), the secrecy outage probability (SOP), the tradeoff relationship, and the secrecy throughput. Analytical and simulation results demonstrate that increasing K leads to the enhancement of the reliability performance, but the degradation of the security performance. This tradeoff also means that there exists an optimal K maximizing the secrecy throughput.

High peak-to-average power ratio (PAPR) and spectral leakage are two main problems of orthogonal frequency division multiplexing (OFDM) systems. For alleviating the above problems, this paper proposes a joint model which efficiently suppresses both PAPR and spectral leakage, by combining serial peak cancellation (SPC) and time-domain N-continuous OFDM (TD-NC-OFDM) in an iterative way. Furthermore, we give an analytical expression of the proposed joint model to analyze the mutual effects between SPC and TD-NC-OFDM. Lastly, simulation results also support that the joint optimization model can obtain notable PAPR reduction and sidelobe suppression performance with low implementation cost.

This letter considers a cognitive radio (CR) network where multiple secondary downlinks coexist with a primary network. The primary user (PU) is assumed to be protected by the interference outage constraint with only channel distribution information (CDI) being available at the secondary users (SUs). The power allocation problem to maximize the sum outage capacity of the SUs under the interference outage constraint and the transmit power constraint is investigated. Due to the difficulty in obtaining the optimal solution, we propose a heuristic power allocation algorithm based on the bisection search method that can guarantee to satisfy both the interference outage and the transmit power constraints. It is shown that the proposed algorithm converges fast and outperforms other reference algorithms.

This paper proposes an effective signal processing scheme using a modulation code with two-dimensional (2D) run-length limited (RLL) constraints for bit-patterned media magnetic recording (BPMR). This 2D signal processing scheme is applied to be one of two-dimensional magnetic recording (TDMR) schemes for shingled magnetic recording on bit patterned media (BPM). A TDMR scheme has been pointed out an important key technology for increasing areal density toward 10Tb/in2. From the viewpoint of 2D signal processing for TDMR, multi-track joint decoding scheme is desirable to increase an effective transfer rate because this scheme gets readback signals from several adjacent parallel tracks and detect recorded data written in these tracks simultaneously. Actually, the proposed signal processing scheme for BPMR gets mixed readback signal sequences from the parallel tracks using a single reading head and these readback signal sequences are equalized to a frequency response given by a desired 2D generalized partial response system. In the decoding process, it leads to an increase in the effective transfer rate by using a single maximum likelihood (ML) sequence detector because the recorded data on the parallel tracks are decoded for each time slot. Furthermore, a new joint pattern-dependent noise-predictive (PDNP) sequence detection scheme is investigated for multi-track recording with media noise. This joint PDNP detection is embed in a ML detector and can be useful to eliminate media noise. Using computer simulation, it is shown that the joint PDNP detection scheme is able to compensate media noise in the equalizer output which is correlated and data-dependent.

This letter investigates the physical layer security for a buffer-aided underlay cooperative cognitive radio network in the presence of an eavesdropper, wherein, the relay is equipped with a buffer so that it can store packets received from the secondary source. To improve the secure performance of cognitive radio networks, we propose a novel cognitive secure link selection scheme which incorporates the instantaneous strength of the wireless links as well as the status of relay's buffer, the proposed scheme adapts the link selection decision on the strongest available link by dynamically switching between relay reception and transmission. Closed-form expressions of secrecy outage probability (SOP) for cognitive radio network is obtained based on the Markov chain. Numerical results demonstrate that the proposed scheme can significantly enhance the secure performance compared to the conventional relay selection scheme.

Ordinal classification is a class of special tasks in machine learning and pattern recognition. As to ordinal classification, there is an ordinal structure among different decision values. The monotonicity constraint between features and decision should be taken into account as the fundamental assumption. However, in real-world applications, this assumption may be not true. Only some candidate features, instead of all, are monotonic with decision. So the existing feature selection algorithms which are designed for nominal classification or monotonic classification are not suitable for ordinal classification. In this paper, we propose a feature selection algorithm for ordinal classification based on considering the non-monotonic and monotonic features separately. We first introduce an assumption of hybrid monotonic classification consistency and define a feature evaluation function to calculate the relevance between the features and decision for ordinal classification. Then, we combine the reported measure and genetic algorithm (GA) to search the optimal feature subset. A collection of numerical experiments are implemented to show that the proposed approach can effectively reduce the feature size and improve the classification performance.