Hyperdimensional computing (HDC) has become popular for light-weight and energy-efficient machine learning, suitable for wearable Internet-of-Things devices and near-sensor or on-device processing. HDC is computationally less complex than traditional deep learning algorithms and achieves moderate to good classification performance. This letter proposes to extend the training procedure in HDC by taking into account not only wrongly classified samples but also samples that are correctly classified by the HDC model but with low confidence. We introduce a confidence threshold that can be tuned for each data set to achieve the best classification accuracy. The proposed training procedure is tested on UCIHAR, CTG, ISOLET, and HAND data sets for which the performance consistently improves compared to the baseline across a range of confidence threshold values. The extended training procedure also results in a shift toward higher confidence values of the correctly classified samples, making the classifier not only more accurate but also more confident about its predictions.

In recent years, single image super-resolution (SISR) deep learning techniques have achieved remarkable improvements in recovering a high-resolution (HR) image from an observed low-resolution (LR) input. Nevertheless, these proposed methods fail in many real-world scenarios since their models are usually trained using a pre-defined degradation process from HR ground truth images to LR ones. To address this issue, new architectures have been proposed focusing on adopting more complicated degradation models to emulate real-world degradation achieving prominent performance but still limited to certain kinds of inputs and dropping considerably in other cases. In this paper, we present a GAN structure for blind super-resolution tasks, applying a technique that has not been very commonly used in SISR proposals: a U-Net architecture as a discriminator of the GAN network. Adding this structural change will encourage the discriminator to focus more on semantic and structural changes between real and fake images and to attend less to domain-preserving perturbations. In addition, the loss function of the generator was modified by adding the LPIPS loss function for the perceptual loss and a per-pixel consistency regularization technique based on the CutMix data augmentation. Numerous novel solutions that have been proposed recently involve powerful deep learning techniques. The proposed model was trained using the DF2K dataset employing a degradation framework for real-world images by estimating blur kernels and real noise distributions to obtain more realistic LR samples. Finally, we present a benchmark comparing our results with other methods in the state-of-the-art. The commonly-used evaluation metrics for image restoration PSNR, SSIM, and LPIPS were used for this evaluation.

This paper concerns the use of Active Queue Management (AQM) to reduce queuing delay. It offers insight into why it has proved hard for a Proportional Integral (PI) controller to remain both responsive and stable while controlling `Classic' TCP flows, such as TCP Reno and Cubic. Due to their non-linearity, the controller's adjustments have to be smaller when the target drop probability is lower. The PI Enhanced (PIE) algorithm attempts to solve this problem by scaling down the adjustments of the controller using a look-up table. Instead, we control an internal variable that is by definition linearly proportional to the load, then post-process it into the required Classic drop probability---in fact we show that the output simply needs to be squared. This allows tighter control, giving responsiveness and stability better or no worse than PIE achieves, but without all its corrective heuristics.

This demo dramatically illustrates how replacing 'Classic' TCP congestion control (Reno, Cubic, etc.) with a 'Scalable' alternative like Data Centre TCP (DCTCP) keeps queuing delay ultra-low; not just for a select few light applications like voice or gaming, but even when a variety of interactive applications all heavily load the same (emulated) Internet access. DCTCP has so far been confined to data centres because it is too aggressive---it starves Classic TCP flows. To allow DCTCP to be exploited on the public Internet, we developed DualQ Coupled Active Queue Management (AQM), which allows the two TCP types to safely co-exist. Visitors can test all these claims. As well as running Web-based apps, they can pan and zoom a panoramic video of a football stadium on a touch-screen, and experience how their personalized HD scene seems to stick to their finger, even though it is encoded on the fly on servers accessed via an emulated delay, representing 'the cloud'. A pair of VR goggles can be used at the same time, making a similar point. The demo provides a dashboard so that visitors can not only experience the interactivity of each application live, but they can also quantify it via a wide range of performance stats, updated live. It also includes controls so visitors can configure different TCP variants, AQMs, network parameters and background loads and immediately test the effect.

This paper presents three new efficient 2×2 block-based algorithms for connected components labeling: a two-scan which assigns provisional labels to blocks, a two-scan which assigns provisional labels to pixels and a one-and-a-half-scan which assigns provisional labels to blocks. A new stripe image representation is designed in order to perform the second pass only through the blocks containing some foreground pixel. We also improved the existing 2×2 block-based algorithms by utilizing information of a pixel during a transition in the mask, which allows checking of four neighbor pixels in the mask at most. Thus, the average number of checking operations needed to inspect the neighbor pixels in the first scan is reduced from 1.459 to 1.156, an improvement of 21%. We conducted experiments using synthetic and real images to evaluate the performance of the proposed methods compared to the existing methods. The proposed block-based one-and-a-half-scan algorithm presents the best performance in the real images dataset, which is composed of 1290 documents. Our block-based two-scan algorithm which assigns provisional labels to pixels showed to be the fastest in the synthetic dataset, especially in high density images.

The proposed Fractional Eigenfaces method is a feature extraction technique for high dimensional data. It is related to Fractional PCA (FPCA), which is based on the theory of fractional covariance matrix, and it is an extension of the classical Eigenfaces. Like FPCA, it computes projections for a low dimensional space from the fractional covariance matrix and similar to the Eigenfaces, it is suited for high dimensional data. Moreover, the proposed technique extends the fractional transformation of the data for more stages of the feature extractions than FPCA. The Fractional Eigenfaces is evaluated in three different face databases. Results show that it achieves a higher accuracy rate than FPCA and Eigenfaces according to the Wilcoxon hypothesis test.

This paper proposes the use of the type-2 fuzzy GMM (T2FGMM) framework in order to improve the verification rates of the standard GMM-UBM text-independent speaker verification system in noisy environments. Based on type-2 fuzzy sets, the T2FGMM framework describes GMMs with uncertain parameters and provides likelihood intervals for them. The proposed method (T2F-GMM-UBM) estimates the parameter intervals using the noisy speeches from the speakers and the Bayesian estimation used in the standard GMM-UBM system. The proposed method was evaluated using the MIT Device Speaker Verification Corpus (MITDSVC) which contains speeches from 48 speakers recorded in three different locations: a quiet office, a mildly noisy lobby, and a busy street intersection. The Equal Error Rate (EER) was computed for each speaker and the mean and standard deviation were analyzed. Although the proposed method did not achieve better performance in the office location, significant improvements were achieved in both lobby and street intersection locations. The improvement in the lobby was 14.21% while in the street intersection location was 10.47%. The left tailed paired Rank Sign Wilcox on Test was also performed in both locations and the p-values found were 0.0127 and 0.0230, respectively. The proposed method proved to have better performance in noisy environments compared to the standard GMM-UBM system.

Block-based algorithms are considered the fastest approach to label connected components in binary images. However, the existing algorithms are two-scan which would need more comparisons if they were used as one-and-a-half-scan algorithms. Here, we proposed a new mask that enables the design of a block-based one-and-a-half-scan algorithm without any extra comparison. Furthermore, three new efficient algorithms for connected components labeling are presented: a block-based two-scan, a pixel-based one-and-a-half-scan and a block-based one-and-a-half-scan. We conducted experiments using synthetic and realistic images to evaluate the performance of the proposed methods compared to the existing methods. The proposed block-based one-and-a-half-scan algorithm presents the best performance in the realistic images dataset composed of 1290 documents. Our block-based two-scan algorithm proved to be the fastest in the synthetic dataset, especially in low density images.

We present a combined features approach for speaker segmentation task. This approach utilizes different acoustic features extracted from audio stream. The Bayesian Information Criterion (BIC) is used for each acoustic feature as a distance measure to verify the merging of two audio segments. An Artificial Neural Network (ANN) combines the time index from each ?BIC with the highest value, and estimates the change point. In the experiments, a data set containing examples with several speakers is used to compare our approach with the Chen and Gopalakrishnan's window-growing-based approach, using different acoustic features sets. The results show an improvement in both the Miss Detection Rate (MDR) and the False Alarm Rate (FAR) compared to the window-growing-based approach.

This paper proposes the use of a type-2 fuzzy framework in the standard GMM-UBM based text-independent speaker verification systems. Based on type-2 fuzzy sets, the framework provides pertinence intervals for the models. The decision process is obtained using a Support Vectors Machine (SVM) that processes the interval likelihoods. A Voice Activity Detection (VAD) algorithm was also used to discard the parts of the speech signal without voice. The proposed method was tested on the MIT Device Speaker Verification Corpus which contains several different mobile devices used in different environments. The result shows the robustness of the system and the improvements in the verification ratios of the T2F-GMM-UBM compared to the classical GMM-UBM based systems.

This paper presents a motion compensation technique applied in the permutation-based digital video encryption and compression method introduced by Socek et al. The encryption method is based in permutations that can improve the spatial correlation on each video frame, making them more compressible by a spatial encoder. However, the compression performance of this method depends on the temporal correlation between consecutive frames and the algorithm does not provide a way to explore non-trivial temporal correlation properly. Consequently, the compression ratio of an encrypted video is very sensitive to the motion in the scene. Here, we propose a motion compensation method to be applied in both encryption and decryption process. Experiments using the H.264 codec show a significant improvement of the compression performance in high motion video sequences.

Pedestrian detection is a very promising area in computer vision, since it enables interesting and a variety of applications such as car assistance, surveillance systems and robot vision. During the last years, a variety of new techniques were proposed which greatly improved the detection rates. However, the performance of such systems rapidly deteriorates when pedestrians are under occlusion. This paper analyze how the detection rates of HOG, HOG-LBP, and two new combinations, HOG-LTP and HOG-LMEBP, are affected when occlusion area are progressively added to pedestrian images. Using the INRIA dataset, occlusions were synthetically generated by merging different sizes of non-pedestrian images from different directions. We show that detection of pedestrian under occlusion can be improved by simply combining features.

Neighborhood coding is a binary image representation method that has been performing successfully for a variety of applications such as features extraction for image recognition, shape descriptor, and image compression. Despite the success of this coding method, the representation lacked a formal notation. Here, we proposed a formal mathematical notation to represent any binary image into a neighborhood coding scheme. Using this representation we also introduce the concept of neighborhood operations, a procedure akin to mathematical morphology, however having a lower computational cost.

The growing amount of information transferred and stored in graphical and video format, and the forthcoming of new devices that have a limited hardware capabilities to either store data or display shades (such as electronic paper), creates a demand for higher computational resources or new techniques that consume less of these resources. In this paper is proposed a new method for video coding called Video Colortoning, which reduces the size of the image representation in a sequence of color image, with a small loss of quality and in real time. The proposed method treats the problem of quantization noise and the flicker effect, optimizes the data for further compression and considers the human visual system regarding the handling of colors.

A social network is composed by communities of individuals or organizations that are connected by a common interest. Online social networking sites like Twitter, Facebook and Orkut are among the most visited sites in the Internet. Presently, there is a great interest in trying to understand the complexities of this type of network from both theoretical and applied point of view. The understanding of these social network graphs is important to improve the current social network systems, and also to develop new applications. Here, we propose a friend recommendation system for social network based on the topology of the network graphs. The topology of network that connects a user to his friends is examined and a local social network called Oro-Aro is used in the experiments. We developed an algorithm that analyses the sub-graph composed by a user and all the others connected people separately by three degree of separation. However, only users separated by two degree of separation are candidates to be suggested as a friend. The algorithm uses the patterns defined by their connections to find those users who have similar behavior as the root user. The recommendation mechanism was developed based on the characterization and analyses of the network formed by the user's friends and friends-of-friends (FOF).

Neighborhood coding was proposed to encode binary images. Previously, this coding scheme presented good results in the problem of handwritten character recognition. In this article, we extended this coding scheme so that it can be applied as an image shape descriptor and in a bilevel image compression method. An algorithm to reduce the number of codes needed to reconstruct the image without loss of information is presented. Using the exactly same set of reduced codes, a lossless compression method and a shape recognition system are proposed. The reduced codes are used with Huffman coding and RLE (Run-Length Encoding) to obtain a compression rate comparable to well-known image compression algorithms such as LZW and CCITT Group 4. For the shape recognition task we applied a template matching algorithm to the set of strings generated by the coding reduction procedure. We tested this method in the MPEG-7 Core Experiment Shape 1 part A2 and the binary image compression challenge database.

We present a neighbourhood vector representation as shape parameter for binary images. This method is based on the pixel neighbourhood relation. Each pixel is transformed into a vector, V = (n, e, s, w), where each element of the vector represents the total number of neighbour pixels in the respective direction, north, east, south, west. A binary object is represented by a set of neighbourhood vectors (NV), in which the information of the shape structure is retained. The k-means and fuzzy c-means clustering methods are used to reduce the total amount of NV and the probability distribution of the reduced NV is used to characterize a class of image. We applied this method for handwritten character recognition, using neural network as classifiers. The results show that the shape parameter can be used as a general method of feature extraction for problems in image processing and pattern recognition. In addition, we present an application of this representation scheme for the neighbourhood image operator.

In this work we present a novel approach to handwritten character recognition which is based on the intuitive way in which characters are written as one or a few continuous lines. Therefore we calculate the zeroth, first and second radial moment as a function of the angle. In practice this is done by dividing the character into 32 angular sections. The three obtained curves can be used for pattern recognition using statistical analysis. The method has been evaluated using the NIST handwritten character data set. At first, a simple chi-square test gave a result of 80.81% recognition rate at zero rejection rate for digits. Using a back-propagation algorithm the recognition rate obtained was 87.54% also at zero rejection rate, showing that the features are sufficient to discriminate the characters.