In this paper, we propose a novel method for road sign detection and recognition in complex scene real world images. Our algorithm consists of four basic steps. First, we employ a regional contrast based bottom-up visual saliency method to highlight the traffic sign regions, which usually have dominant color contrast against the background. Second, each type of traffic sign has special color distribution, which can be explored by top-down visual saliency to enhance the detection precision and to classify traffic signs into different categories. A bag-of-words (BoW) model and a color name descriptor are employed to compute the special-class distribution. Third, the candidate road sign blobs are extracted from the final saliency map, which are generated by combining the bottom-up and the top-down saliency maps. Last, the color and shape cues are fused in the BoW model to express blobs, and a support vector machine is employed to recognize road signs. Experiments on real world images show a high success rate and a low false hit rate and demonstrate that the proposed framework is applicable to prohibition, warning and obligation signs. Additionally, our method can be applied to achromatic signs without extra processing.

Localized content-based image retrieval (LCBIR) has emerged as a hot topic more recently because in the scenario of CBIR, the user is interested in a portion of the image and the rest of the image is irrelevant. In this paper, we propose a novel region-level relevance feedback method to solve the LCBIR problem. Firstly, the visual attention model is employed to measure the regional saliency of each image in the feedback image set provided by the user. Secondly, the regions in the image set are constructed to form an affinity matrix and a novel propagation energy function is defined which takes both low-level visual features and regional significance into consideration. After the iteration, regions in the positive images with high confident scores are selected as the candidate query set to conduct the next-round retrieval task until the retrieval results are satisfactory. Experimental results conducted on the SIVAL dataset demonstrate the effectiveness of the proposed approach.

A CMOS voltage-to-current converter in weak inversion is presented in this Letter. It can operate for low supply voltage and its power consumption is also low. As the input voltage varies from -0.15 V to 0.15 V, the measured maximum linearity error for the proposed voltage-to-current converter, is about 3.35%. Its power consumption is only 26 µW under the supply voltage of 2 V. The proposed voltage-to-current converter has been fabricated in a 0.5 µm N-well CMOS 2P2M process. The proposed circuit is expected to be useful in analog signal processing applications.

Face verification in the presence of age progression is an important problem that has not been widely addressed. Despite appearance changes for same person due to aging, they are more similar compared to facial images from different individuals. Hence, we design common and adapted vocabularies, where common vocabulary describes contents of general population and adapted vocabulary represents specific characteristics of one of image facial pairs. And the other image is characterized with a concatenation histogram of common and adapted visual words counts, termed as “age-invariant distinctive representation”. The representation describes whether the image content is best modeled by the common vocabulary or the corresponding adapted vocabulary, which is further used to accomplish the face verification. The proposed approach is tested on the FGnet dataset and a collection of real-world facial images from identification card. The experimental results demonstrate the effectiveness of the proposed method for verification of identity at a modest computational cost.

In this paper, the application of an unsupervised parallel approach called the Fuzzy Hopfield Neural Network (FHNN) for vector qunatization in image compression is proposed. The main purpose is to embed fuzzy reasoning strategy into neural networks so that on-line learning and parallel implementation for codebook design are feasible. The object is to cast a clustering problem as a minimization process where the criterion for the optimum vector qunatization is chosen as the minimization of the average distortion between training vectors. In order to generate feasible results, a fuzzy reasoning strategy is included in the Hopfield neural network to eliminate the need of finding weighting factors in the energy function that is formulated and based on a basic concept commonly used in pattern classification, called the "within-class scatter matrix" principle. The suggested fuzzy reasoning strategy has been proven to allow the network to learn more effectively than the conventional Hopfield neural network. The FHNN based on the within-class scatter matrix shows the promising results in comparison with the c-means and fuzzy c-means algorithms.

We propose a compact magnetic tunnel junction (MTJ) model for circuit simulation by de-facto standard SPICE in this paper. It is implemented by Verilog-A language which makes it easy to simulate MTJs with other standard devices. Based on the switching probability, we smoothly connect the adiabatic precessional model and the thermal activation model by using an interpolation technique based on the cubic spline method. We can predict the switching time after a current is applied. Meanwhile, we use appropriate physical models to describe other MTJ characteristics. Simulation results validate that the model is consistent with experimental data and effective for MTJ/CMOS hybrid circuit simulation.

The multi-step prediction model based on partial least squares (PLS) is established to predict short-term load series with high embedding dimension in this paper, which refrains from cumulative error with local single-step linear model, and can cope with the multi-collinearity in the reconstructed phase space. In the model, PLS is used to model the dynamic evolution between the phase points and the corresponding future points. With research on the PLS theory, the model algorithm is put forward. Finally, the actual load series are used to test this model, and the results show that the model plays well in chaotic time series prediction, even if the embedding dimension is selected a big value.

Model-based sensorless control of permanent magnet synchronous motor (PMSM) is promising for high-speed operation to estimate motor state, which is the speed and the position of the rotor, via electric signals of the stator, beside the inevitable fact that estimation accuracy is degraded by electromagnet interference (EMI) from switching devices of the converter. In this paper, the simulation system based on Luenberger observer and phase-locked loop (PLL) has been established, analyzing impacts of EMI on motor state estimations theoretically, exploring influences of EMI with different cutoff frequency, rated speeds, frequencies and amplitudes. The results show that Luenberger observer and PLL have strong immunity, which enable PMSM can still operate stably even under certain degrees of interference. EMI produces sideband harmonics that enlarge pulsation errors of speed and position estimations. Additionally, estimation errors are positively correlated with cutoff frequency of low-pass filter and the amplitude of EMI, and negatively correlated with rated speed of the motor and the frequency of EMI.  When the frequency is too high, its effects on motor state estimations are negligible. This work contributes to the comprehensive understanding of how EMI affects motor state estimations, which further enhances practical application of sensorless PMSM.

When new users join a recommender system, traditional approaches encounter challenges in accurately understanding their interests due to the absence of historical user behavior data, thus making it difficult to provide personalized recommendations. Currently, two main methods are employed to address this issue from different perspectives. One approach is centered on meta-learning, enabling models to adapt faster to new tasks by sharing knowledge and experiences across multiple tasks. However, these methods often overlook potential improvements based on cross-domain information. The other method involves cross-domain recommender systems, which transfer learned knowledge to different domains using shared models and transfer learning techniques. Nonetheless, this approach has certain limitations, as it necessitates a substantial amount of labeled data for training and may not accurately capture users’ latent preferences when dealing with a limited number of samples. Therefore, a crucial need arises to devise a novel method that amalgamates cross-domain information and latent preference extraction to address this challenge. To accomplish this objective, we propose a Cross-domain Recommender System based on Domain Knowledge Transferor and Latent Preference Extractor (TECDR).  In TECDR, we have designed a Latent Preference Extractor that transforms user behaviors into representations of their latent interests in items. Additionally, we have introduced a Domain Knowledge Transfer mechanism for transferring knowledge and patterns between domains. Moreover, we leverage meta-learning-based optimization methods to assist the model in adapting to new tasks. The experimental results from three cross-domain scenarios demonstrate that TECDR exhibits outstanding performance across various cross-domain recommender scenarios.

Spatial modulation (SM) is a type of multiple-input multiple-output (MIMO) technology that provides several benefits over traditional MIMO systems. SM-MIMO is characterized by its unique transmission principle, which results in lower costs, enhanced spectrum utilization, and reduced inter-channel interference. To optimize channel estimation performance over frequency-selective channels in the spatial modulation system, cross Z-complementary pairs (CZCPs) have been proposed as training sequences. The zero correlation zone (ZCZ) properties of CZCPs for auto-correlation sums and cross-correlation sums enable them to achieve optimal channel estimation performance. In this paper, we systematically construct CZCPs based on binary Golay complementary pairs and binary Golay complementary pairs via Turyn’s method. We employ a special matrix operation and concatenation method to obtain CZCPs with new lengths 2M + N and 2(M + L), where M and L are the lengths of binary GCP, and N is the length of binary GCP via Turyn’s method. Further, we obtain the perfect CZCP with new length 4N and extend the lengths of CZCPs.

This letter presented several new constructions of complementary sets (CSs) with flexible sequence lengths using matrix transformations. The constructed CSs of size 4 have different lengths, namely N + L and 2N + L, where N and L are the lengths for which complementary pairs exist. Also, presented CSs of size 8 have lengths N + P, P + Q and 2P + Q, where N is length of complementary pairs, P and Q are lengths of CSs of size 4 exist. The achieved designs can be easily extended to a set size of 2n+2 by recursive method. The proposed constructions generalize some previously reported constructions along with generating CSs under fewer constraints.

Fish skin color detection plays a critical role in aquaculture. However, challenges arise from image color cast and the limited dataset, impacting the accuracy of the skin color detection process. To address these issues, we proposed a novel fish skin color detection method, termed VH-YOLOv5s. Specifically, we constructed a dataset for fish skin color detection to tackle the limitation posed by the scarcity of available datasets. Additionally, we proposed a Variance Gray World Algorithm (VGWA) to correct the image color cast. Moreover, the designed Hybrid Spatial Pyramid Pooling (HSPP) module effectively performs multi-scale feature fusion, thereby enhancing the feature representation capability. Extensive experiments have demonstrated that VH-YOLOv5s achieves excellent detection results on the Plectropomus leopardus skin color dataset, with a precision of 91.7%, recall of 90.1%, mAP@0.5 of 95.2%, and mAP@0.5:0.95 of 57.5%. When compared to other models such as Centernet, AutoAssign, and YOLOX-s, VH-YOLOv5s exhibits superior detection performance, surpassing them by 2.5%, 1.8%, and 1.7%, respectively. Furthermore, our model can be deployed directly on mobile phones, making it highly suitable for practical applications.

Fast switching speed, low power consumption, and good stability are some of the important properties of spin transfer torque assisted voltage controlled magnetic anisotropy magnetic tunnel junction (STT-assisted VCMA-MTJ) which makes the non-volatile full adder (NV-FA) based on it attractive for Internet of Things. However, the effects of process variations on the performances of STT-assisted VCMA-MTJ and NV-FA will be more and more obvious with the downscaling of STT-assisted VCMA-MTJ and the improvement of chip integration. In this paper, a more accurate electrical model of STT-assisted VCMA-MTJ is established on the basis of the magnetization dynamics and the process variations in film growth process and etching process. In particular, the write voltage is reduced to 0.7 V as the film thickness is reduced to 0.9 nm. The effects of free layer thickness variation (γtf) and oxide layer thickness variation (γtox) on the state switching as well as the effect of tunnel magnetoresistance ratio variation (β) on the sensing margin (SM) are studied in detail. Considering that the above process variations follow Gaussian distribution, Monte Carlo simulation is used to study the effects of the process variations on the writing and output operations of NV-FA. The result shows that the state of STT-assisted VCMA-MTJ can be switched under -0.3%≤γtf≤6% or -23%≤γtox≤0.2%. SM is reduced by 16.0% with β increases from 0 to 30%. The error rates of writing ‘0’ in the NV-FA can be reduced by increasing Vb1 or increasing positive Vb2. The error rates of writing ‘1’ can be reduced by increasing Vb1 or decreasing negative Vb2. The reduction of the output error rates can be realized effectively by increasing the driving voltage (Vdd).

In this paper, an adaptive updating probabilistic model is proposed to track an object in real-world environment that includes motion blur, illumination changes, pose variations, and occlusions. This model adaptively updates tracker with the searching and updating process. The searching process focuses on how to learn appropriate tracker and updating process aims to correct it as a robust and efficient tracker in unconstrained real-world environments. Specifically, according to various changes in an object's appearance and recent probability matrix (TPM), tracker probability is achieved in Expectation-Maximization (EM) manner. When the tracking in each frame is completed, the estimated object's state is obtained and then fed into update current TPM and tracker probability via running EM in a similar manner. The highest tracker probability denotes the object location in every frame. The experimental result demonstrates that our method tracks targets accurately and robustly in the real-world tracking environments.

Appearance changes conform to certain rules for a same person,while for different individuals the changes are uncontrolled. Hence, this paper studies the age progression rules to tackle face verification task. The age progression rules are discovered in the difference space of facial image pairs. For this, we first represent an image pair as a matrix whose elements are the difference of a set of visual words. Thereafter, the age progression rules are trained using Support Vector Machine (SVM) based on this matrix representation. Finally, we use these rules to accomplish the face verification tasks. The proposed approach is tested on the FGnet dataset and a collection of real-world images from identification card. The experimental results demonstrate the effectiveness of the proposed method for verification of identity.

The need for efficient movement and precise location of robots in intelligent robot control systems within complex buildings is becoming increasingly important. This paper proposes an indoor positioning and communication platform using Fluorescent Light Communication (FLC) employing a newly developed nine-channel receiver, and discusses a new location estimation method using FLC, that involves a simulation model and coordinate calculation formulae. A series of experiments is performed. Distance errors of less than 25 cm are achieved. The enhanced FLC system yields benefits such as greater precision and ease of use.

This paper presents a fully integrated yet compact receiver front-end for Sub-GHz applications such as Internet-of-Things (IoT). The low noise amplifier (LNA) matching network leverages an inductance boosting technique. A relatively small on-chip inductor with a compact area achieves impedance matching in such a low frequency. Moreover, a passive-mixer-first mode bypasses the LNA to extend the receiver dynamic-range. The passive mixer provides matching to the 50Ω antenna interface to eliminate the need for additional passive components. Therefore, the receiver can be fully-integrated without any off-chip matching components. The flipped-voltage-follower (FVF) cell is adopted in the low pass filter (LPF) and the variable gain amplifier (VGA) for its high linearity and low power consumption. Fabricated in 65nm LP CMOS process, the proposed receiver front-end occupies 0.37mm2 core area, with a tolerable input power ranging from -91.5dBm to -1dBm for 500kbps GMSK signal at 924MHz frequency. The power consumption is 1mW power under a 1.2V supply.

This paper proposes the Extended Bag-of-Visterms (EBOV) to represent semantic scenes. In previous methods, most representations are bag-of-visterms (BOV), where visterms referred to the quantized local texture information. Our new representation is built by introducing global texture information to extend standard bag-of-visterms. In particular we apply the adaptive weight to fuse the local and global information together in order to provide a better visterm representation. Given these representations, scene classification can be performed by pLSA (probabilistic Latent Semantic Analysis) model. The experiment results show that the appropriate use of global information improves the performance of scene classification, as compared with BOV representation that only takes the local information into account.

The phenomenon of stochastic resonance (SR) in a mono-threshold-system-based detector (MTD) with additive background noise and multiplicative external noise is investigated. On the basis of maximum a posteriori probability (MAP) criterion, we deal with the binary signal transmission in four scenarios. The performance of the MTD is characterized by the probability of error detection, and the effects of system threshold and noise intensity on detectability are discussed in this paper. Similar to prior studies that focus on additive noises, along with increases in noise intensity, we also observe a non-monotone phenomenon in the multiplicative ways. However, unlike the case with the additive noise, optimal multiplicative noises all tend toward infinity for fixed additive noise intensities. The results of our model are potentially useful for the design of a sensor network and can help one to understand the biological mechanism of synaptic transmission.

Transfer learning extracts useful information from the related source domain and leverages it to promote the target learning. The effectiveness of the transfer was affected by the relationship among domains. In this paper, a novel multi-source transfer learning based on multi-similarity was proposed. The method could increase the chance of finding the sources closely related to the target to reduce the “negative transfer” and also import more knowledge from multiple sources for the target learning. The method explored the relationship between the sources and the target by multi-similarity metric. Then, the knowledge of the sources was transferred to the target based on the smoothness assumption, which enforced that the target classifier shares similar decision values with the relevant source classifiers on the unlabeled target samples. Experimental results demonstrate that the proposed method can more effectively enhance the learning performance.