Optimizing embedded software is a problem having scientific and practical signification. Optimizing embedded software can be done in different phases of the software life cycle under different optimal conditions. Most studies of embedded software optimization are done in forward engineering and these studies have not given an overall model for the optimization problem of embedded software in both forward engineering and reverse engineering. Therefore, in this paper, we propose a new approach to embedded software optimization based on reverse engineering. First, we construct an overall model for the embedded software optimization in both forward engineering and reverse engineering and present a process of embedded software optimization in reverse engineering. The main idea of this approach is that decompiling executable code to source code, converting the source code to models and optimizing embedded software under different levels such as source code and model. Then, the optimal source code is recompiled. To develop this approach, we present two optimization techniques such as optimizing power consumption of assembly programs based on instruction schedule and optimizing performance based on alternating equivalent expressions.

With the development of COMPASS system, finding suitable and efficient multiplexing solutions have become important for the system signal design. In this paper, based on the alternative BOC (AltBOC) modulation technique, the multiplexing scheme for COMPASS Phase II B1 signals is proposed. Then, to combine all COMPASS Phase III (CP III) B1 components into a composite signal with constant envelope, the generalized majority voting (GMV) technique is employed based on the characteristics of CP III B1 signals. The proposed multiplexing schemes also provide potential opportunities for GNSS modernization and construction, such as GPS, Galileo, etc.

In this paper, we propose a new method based on copula theory to evaluate the detection performance of a distributed-processing multistatic radar system (DPMRS). By applying the Gaussian copula to model the dependence of local decisions in a DPMRS as well as data fusion rules of AND, OR, and K/N, the performance of a DPMRS for detecting Swerling fluctuating targets can be easily evaluated even under non-Gaussian clutter with a nonuniform dependence matrix. The reliability and flexibility of this method are validated by applying the proposed method to a previous problem by other authors, and our other investigation results indicate its high potential for evaluating DPMRS performance in various cases involving different models of target and clutter.

Quantifying user satisfaction is essential, because the results can help service providers deliver better services. In this work, we propose a generalizable methodology, based on survival analysis, to quantify user satisfaction in terms of session times, i.e., the length of time users stay with an application. Unlike subjective human surveys, our methodology is based solely on passive measurement, which is more cost-efficient and better able to capture subconscious reactions. Furthermore, by using session times, rather than a specific performance indicator, such as the level of distortion of voice signals, the effects of other factors like loudness and sidetone, can also be captured by the developed models. Like survival analysis, our methodology is characterized by low complexity and a simple model-developing process. The feasibility of our methodology is demonstrated through case studies of ShenZhou Online, a commercial MMORPG in Taiwan, and the most prevalent VoIP application in the world, namely Skype. Through the model development process, we can also identify the most significant performance factors and their impacts on user satisfaction and discuss how they can be exploited to improve user experience and optimize resource allocation.

This letter proposes a novel scheme of joint antenna combination and symbol detection in multi-input multi-output (MIMO) systems, which simultaneously determines the antenna combination coefficients to lower the RF chains and designs the minimum bit error rate (MBER) detector to mitigate the interference. The joint decision statistic, however, is highly nonlinear and the particle swarm optimization (PSO) algorithm is employed to reduce the computational overhead. Simulations show that the new approach yields satisfactory performance with reduced computational overhead compared with pervious works.

In this paper, we present an approach for 3D face recognition based on Multi-level Partition of Unity (MPU) Implicits under pose and expression variations. The MPU Implicits are used for reconstructing 3D face surface in a hierarchical way. Three landmarks, nose, left eyehole and right eyehole, can be automatically detected with the analysis of curvature features at lower levels of reconstruted face. Thus, the 3D faces are initially registered to a common coordinate system based on the three landmarks. A variant of Iterative Closest Point (ICP) algorithm is proposed for matching the point surface of a given probe face to the implicits face surface in the gallery. To evaluate the performance of our approach for 3D face recognition, we perform an experiment on GavabDB face database. The results of the experiment show that our method based on MPU Implicits and Adaptive ICP has great capability for 3D face recognition under pose and expression variations.

Increased demand for DNS privacy has driven the creation of several encrypted DNS protocols, such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Recently, DoT and DoH have been deployed by some vendors like Google and Cloudflare. This paper addresses privacy leakage in these three encrypted DNS protocols (especially DoQ) with different DNS recursive resolvers (Google, NextDNS, and Bind) and DNS proxy (AdGuard). More particularly, we investigate encrypted DNS traffic to determine whether the adversary can infer the category of websites users visit for this purpose. Through analyzing packet traces of three encrypted DNS protocols, we show that the classification performance of the websites (i.e., user's privacy leakage) is very high in terms of identifying 42 categories of the websites both in public (Google and NextDNS) and local (Bind) resolvers. By comparing the case with cache and without cache at the local resolver, we confirm that the caching effect is negligible as regards identification. We also show that discriminative features are mainly related to the inter-arrival time of packets for DNS resolving. Indeed, we confirm that the F1 score decreases largely by removing these features. We further investigate two possible countermeasures that could affect the inter-arrival time analysis in the local resolver: AdBlocker and DNS prefetch. However, there is no significant improvement in results with these countermeasures. These findings highlight that information leakage is still possible even in encrypted DNS traffic regardless of underlying protocols (i.e., HTTPS, TLS, QUIC).

Pattern extraction is an indispensable step in bare printed circuit board (PCB) inspection and plays an important role in automatic inspection system design. A good approach for pattern definition and extraction will make the following PCB diagnosis easy and efficient. The window-based technique has great potential in PCB patterns extraction due to its simplicity. The conventional window-based pattern extraction methods, such as Small Seeds Window Extraction method (SSWE) and Large Seeds Window Extraction method (LSWE), have the problems of losing some useful copper traces and splitting slanted-lines into too many small similar windows. These methods introduce the difficulty and computation intensive in automatic inspection. In this paper, a novel method called Contour Based Window Extraction (CBWE) algorithm is proposed for improvement. In comparison with both SSWE and LSWE methods, the CBWE algorithm has several advantages in application. Firstly, all traces can be segmented and enclosed by a valid window. Secondly, the type of the entire horizontal or vertical line of copper trace is preserved. Thirdly, the number of the valid windows is less than that extracted by SSWE and LSWE. From the experimental results, the proposed CBWE algorithm is demonstrated to be very effective in basic pattern extraction from bare PCB image analysis.

With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

The paper reports a compact and high performance dual-band bandpass filter (BPF) using two types of dual-mode resonators. The dual mode cross shaped resonator and the three dual mode ring resonators in the designed dual-band BPF are excited to control the first and second passband, respectively. It is shown that the designed and fabricated dual-band BPF has narrow bandwidths and very sharp attenuation rate due to the existence of the transmission zeros. The frequency response of the designed dual-band BPF shows good agreement between the simulations and experiments.

Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.

A novel algorithm is proposed for estimating the direction of arrival (DOA) of linear frequency modulated (LFM) signals for the uniform circular array (UCA). Firstly, the UCA is transformed into an equivalent virtual uniform linear array (ULA) using the mode-space algorithm. Then, the short time Fourier transform (STFT) of each element's output is worked out. We can obtain the spatial time-frequency distribution matrix of the virtual ULA by selecting the single-source time-frequency (t-f) points in the t-f plane and then get the signal subspace of the array. The characteristics nature of the Bessel function allow us to obtain the multiple invariance (MI) of the virtual ULA. So the multiple rotational invariant equation of the array can be obtained and its closed-form solution can be worked out using the multi-least-squares (MLS) criterion. Finally, the two dimensional (2-D) DOA estimation of LFM signals for UCA can be obtained. Numerical simulation results illustrate that the UCA-STFT-MI-ESPRIT algorithm proposed in this paper can improve the estimation precision greatly compared with the traditional ESPRIT-like algorithms and has much lower computational complexity than the MUSIC-like algorithms.

The letter proposes a novel subsampling-based digital image watermarking scheme resisting the permutation attack. The subsampling-based watermarking schemes have drawn great attention for their convenience and effectiveness in recent years, but the traditional subsampling-based watermarking schemes are very vulnerable to the permutation attack. In this letter, the watermark information is embedded in the average values of the 1-level DWT coefficients to resist the permutation attack. The concrete embedding process is achieved by the quantization-based method. Experimental results show that the proposed scheme can resist not only the permutation attack but also some common image processing attacks.

The investigation presents a low cost and low insertion loss X-band dual mode bandpass filter (BPF) based on inexpensive commercial FR4 substrate. The proposed filter at a central frequency f0 of 11.3 GHz has high filter performance filter with a fractional bandwidth of 14%, the insertion loss of -2.7 dB, and two transmission zeros. The designed procedures are presented in this letter and the fabricated filter verifies the proposed designed concept.

A minor component analysis approach based on the generalized sidelobe canceler is presented to realize the blind suppression of multiple-access interference in multicarrier code division multiple access systems. With a rough user-code and timing estimations, this proposed method of less computation performs the same as minimum mean square error detectors and outperforms existing blind detectors. Simulation results illustrate the effectiveness of the blind multiuser detection.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

Maneuvering target tracking under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions has received considerable interest in the last decades. In this paper, a hierarchical interacting multiple model (HIMM) method is proposed for estimating target position under mixed LOS/NLOS conditions. The proposed HIMM is composed of two layers with Markov switching model. The purpose of the upper layer, which is composed of two interacting multiple model (IMM) filters in parallel, is to handle the switching between the LOS and the NLOS environments. To estimate the target kinetic variables (position, speed and acceleration), the unscented Kalman filter (UKF) with the current statistical (CS) model is used in the lower-layer. Simulation results demonstrate the effectiveness and superiority of the proposed method, which obtains better tracking accuracy than the traditional IMM.

Traditional low-rank feature lose the temporal information among action sequence. To obtain the temporal information, we split an action video into multiple action subsequences and concatenate all the low-rank features of subsequences according to their time order. Then we recognize actions by learning a novel dictionary model from concatenated low-rank features. However, traditional dictionary learning models usually neglect the similarity among the coding coefficients and have bad performance in dealing with non-linearly separable data. To overcome these shortcomings, we present a novel similarity constrained discriminative kernel dictionary learning for action recognition. The effectiveness of the proposed method is verified on three benchmarks, and the experimental results show the promising results of our method for action recognition.

Sentence similarity computation is an increasingly important task in applications of natural language processing such as information retrieval, machine translation, text summarization and so on. From the viewpoint of information theory, the essential attribute of natural language is that the carrier of information and the capacity of information can be measured by information content which is already successfully used for word similarity computation in simple ways. Existing sentence similarity methods don't emphasize the information contained by the sentence, and the complicated models they employ often need using empirical parameters or training parameters. This paper presents a fully unsupervised computational model of sentence semantic similarity. It is also a simply and straightforward model that neither needs any empirical parameter nor rely on other NLP tools. The method can obtain state-of-the-art experimental results which show that sentence similarity evaluated by the model is closer to human judgment than multiple competing baselines. The paper also tests the proposed model on the influence of external corpus, the performance of various sizes of the semantic net, and the relationship between efficiency and accuracy.

A novel low insertion-loss and wideband microstrip bandpass filter has been designed and tested. The basic configuration of this novel dual-mode filter is a square ring resonator with direct-connected orthogonal feed lines, and dual-perturbation elements are introduced within the resonator at symmetrical location. The effects of the size of the perturbation element are studied. A new filter having wider bandwidth and transmission zeros are presented. The proposed filter responses are in good agreement with the simulations and experiments.