A Novel FDLSR-Based Technique for View-Independent Vehicle Make and Model Recognition.

Vehicle make and model recognition (VMMR) is an important aspect of intelligent transportation systems (ITS). In VMMR systems, surveillance cameras capture vehicle images for real-time vehicle detection and recognition. These captured images pose challenges, including shadows, reflections, changes in weather and illumination, occlusions, and perspective distortion. Another significant challenge in VMMR is the multiclass classification. This scenario has two main categories: (a) multiplicity and (b) ambiguity. Multiplicity concerns the issue of different forms among car models manufactured by the same company, while the ambiguity problem arises when multiple models from the same manufacturer have visually similar appearances or when vehicle models of different makes have visually comparable rear/front views. This paper introduces a novel and robust VMMR model that can address the above-mentioned issues with accuracy comparable to state-of-the-art methods. Our proposed hybrid CNN model selects the best descriptive fine-grained features with the help of Fisher Discriminative Least Squares Regression (FDLSR). These features are extracted from a deep CNN model fine-tuned on the fine-grained vehicle datasets Stanford-196 and BoxCars21k. Using ResNet-152 features, our proposed model outperformed the SVM and FC layers in accuracy by 0.5% and 4% on Stanford-196 and 0.4 and 1% on BoxCars21k, respectively. Moreover, this model is well-suited for small-scale fine-grained vehicle datasets.

An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC.

Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs.

Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network.

Speech emotion recognition (SER) plays a significant role in human-machine interaction. Emotion recognition from speech and its precise classification is a challenging task because a machine is unable to understand its context. For an accurate emotion classification, emotionally relevant features must be extracted from the speech data. Traditionally, handcrafted features were used for emotional classification from speech signals; however, they are not efficient enough to accurately depict the emotional states of the speaker. In this study, the benefits of a deep convolutional neural network (DCNN) for SER are explored. For this purpose, a pretrained network is used to extract features from state-of-the-art speech emotional datasets. Subsequently, a correlation-based feature selection technique is applied to the extracted features to select the most appropriate and discriminative features for SER. For the classification of emotions, we utilize support vector machines, random forests, the k-nearest neighbors algorithm, and neural network classifiers. Experiments are performed for speaker-dependent and speaker-independent SER using four publicly available datasets: the Berlin Dataset of Emotional Speech (Emo-DB), Surrey Audio Visual Expressed Emotion (SAVEE), Interactive Emotional Dyadic Motion Capture (IEMOCAP), and the Ryerson Audio Visual Dataset of Emotional Speech and Song (RAVDESS). Our proposed method achieves an accuracy of 95.10% for Emo-DB, 82.10% for SAVEE, 83.80% for IEMOCAP, and 81.30% for RAVDESS, for speaker-dependent SER experiments. Moreover, our method yields the best results for speaker-independent SER with existing handcrafted features-based SER approaches.

Fault-Tolerant Network-On-Chip Router Architecture Design for Heterogeneous Computing Systems in the Context of Internet of Things.

Network-on-chip (NoC) architectures have become a popular communication platform for heterogeneous computing systems owing to their scalability and high performance. Aggressive technology scaling makes these architectures prone to both permanent and transient faults. This study focuses on the tolerance of a NoC router to permanent faults. A permanent fault in a NoC router severely impacts the performance of the entire network. Thus, it is necessary to incorporate component-level protection techniques in a router. In the proposed scheme, the input port utilizes a bypass path, virtual channel (VC) queuing, and VC closing strategies. Moreover, the routing computation stage utilizes spatial redundancy and double routing strategies, and the VC allocation stage utilizes spatial redundancy. The switch allocation stage utilizes run-time arbiter selection. The crossbar stage utilizes a triple bypass bus. The proposed router is highly fault-tolerant compared with the existing state-of-the-art fault-tolerant routers. The reliability of the proposed router is 7.98 times higher than that of the unprotected baseline router in terms of the mean-time-to-failure metric. The silicon protection factor metric is used to calculate the protection ability of the proposed router. Consequently, it is confirmed that the proposed router has a greater protection ability than the conventional fault-tolerant routers.

Iatrogenic Cushing Syndrome And Hypertension In An Infant Girl Due To Use Of Nappy Rash Cream Containing A Potent Topical Steroid.

Eleven months old female patient presented to paediatric out patient with parents concerned about her facial swelling. Upon examination child's weight and height for age were normal on her percentiles, she had a cushingoid facies with plethoric cheeks (Figure-1,2) though generalized oedema was absent and there was centripetal obesity with some muscle wasting (Figure-3,4). Systemic examination was normal excluding blood pressure which was high for her age. Electrolytes and cortisol levels were normal. On further inquiry it was revealed that she had been using a nappy rash cream containing a potent steroid, i.e., fluticasone for 2 months and this was identified as a cause for her cushingoid features.

Incorporating Noise Robustness in Speech Command Recognition by Noise Augmentation of Training Data.

The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark.

Vehicle Make and Model Recognition using Bag of Expressions.

Vehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.

Factors affecting adherence to treatment of psoriasis: comparing biologic therapy to other modalities.

OBJECTIVES: This study evaluated self-reported patient adherence to different types of treatment in psoriasis and factors that affect adherence. PATIENTS AND METHODS: Patients attending a Dermatology Department for treatments of psoriasis completed a questionnaire about adherence to each of their therapies, Self-assessed Psoriasis Area and Severity Index (SAPASI) and Dermatology Life Quality Index (DLQI). RESULTS: Hundred and six patients participated, 98 on topical treatments, 43 on oral systemic therapies, 39 on phototherapy and 29 were on biologic therapies. The overall rate of self-reported treatment adherence was 85.8%. There was a significant relationship between the types of treatment (topical, oral systemic, phototherapy and biologic therapy) and the number of combinations of treatments and adherence. Adherence ranked significantly better on biologic therapies 100%, followed by oral therapy 96%, phototherapy 93% and then topical therapy 75%. Being too busy, being fed up and cigarette smoking were associated with reduced adherence. About 56.8% of patients reported that messiness of treatment prevented them from adhering. Patients with mild psoriasis and those with DLQI of 5 or less adhered less to topical therapy. CONCLUSIONS: There is a significant relationship between the types of treatment (topical, oral systemic, phototherapy and biologic therapy) and the number of combinations of treatments and adherence.

Metastatic Crohn's disease of the penis: two cases.

Metastatic Crohn's disease is a rare inflammatory process that is non-contiguous from the bowel. It can affect the penis and is variable in presentation and onset in relation to bowel symptoms. It has been treated with oral, topical, systemic, and surgical therapies. We describe our experience with two cases of penile metastatic Crohn's disease and their management in comparison with other cases described in the literature. Both our patients were of the lymphoedematous type and had sexual and voiding dysfunction. They were treated with topical and intra-lesional steroids and circumcision after unsuccessful systemic treatments.