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Smart Car
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 4) |
| Halaman | : | 46-58 |
| Abstrak | : | In contrast to a traditional mechanical car, the Smart Car is a highly computerized automobile featuring ubiquitous computing, intuitive human-computer interaction and an open application platform. In this paper, we propose an advanced Smart Car demonstration platform with a transparent windshield display and various motion sensors where drivers can manipulate a variety of car-appropriate applications in augmented reality. Similar to smartphones, drivers can customize their Smart Car through free downloads of car-appropriate applications according to their needs. Additionally, three potential car-appropriate applications related to computer vision are investigated and implemented in our platform for increased driving safety. The first and second carappropriate applications aim to enhance the driving visual field by restoring the low-visibility scenes captured during inclement-weather or nighttime driving conditions to be high-visibility ones, respectively, and display them on a transparent windshield display. We also survey pedestrian tracking techniques that combine multiple driving recorders' information as a mobile surveillance network, including one proposed framework we have developed as the third car-appropriate application. By embedding these carappropriate applications, the Smart Car has the potential to increase safety of driving conditions both in daytime and nighttime, even in bad weather. |
Drone as a Service: Research Challenges and Directions
| Pengarang | : | Ali Hamdi; Balsam Alkouz; Babar Shahzaad; Athman Bouguettaya; Azadeh Ghari Neiat; Flora Salim; Du Yong Kim |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 113 (No. 5) |
| Halaman | : | 416-442 |
| Abstrak | : | We conduct a survey on drones used as a service, denoted as drone-as-a-service (DaaS). We develop a novel taxonomy based on DaaS functions, research tasks, and application domains. We provide a discussion on drones and their associated capabilities based on their type of use. We propose a three-layered DaaS system architecture that vertically integrates cloud computing, drones, and services as a reference framework to compare existing drone service implementations. Additionally, we propose a representative uncertainty-aware DaaS model for delivery scenarios, illustrating how service definitions can incorporate both functional and nonfunctional attributes under dynamic environmental conditions. Finally, we identify and discuss future research directions and open problems related to the use of drones for service delivery. |
PASUKAN GERAK CEPAT AMERIKA SERIKAT, OPERASI MILITER DI MANA?
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-2, PEBRUARI (No. 2) |
| Halaman | : | 112-124 |
| Abstrak | : | - |
LATAR BELAKANG, PROSPEK DAN IMPLIKASI-IMPLIKASI PERANG ANTARA IRAK DAN IRAN
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-2, PEBRUARI (No. 2) |
| Halaman | : | 95-111 |
| Abstrak | : | - |
Super-Sparse Learning in Similarity Spaces
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 4) |
| Halaman | : | 36-45 |
| Abstrak | : | In several applications, input samples are more nat- urally represented in terms of similarities between each other, rather than in terms of feature vectors. machine-learning In these settings. algorithms ally demanding, can become very computation- as they may require matching the test samples against a very large set of reference prototypes. To mitigate this issue, different approaches have been developed to reduce the number of required reference prototypes. approaches select Current reduction a small subset of representative prototypes in the space induced by the similarity measure, and then sepa- rately train the classification function on the reduced subset. However, decoupling these two steps may not allow reducing the number of prototypes effectively without accuracy. We compromising overcome this limitation by jointly learning the classification function along with an optimal set of virtual pro- totypes, whose number can be either fixed a priori or opti- mized according to application-specific criteria. Creating a super-sparse set of virtual prototypes solutions, drastically provides much sparser reducing complexity expense of a slightly at test time, at the increased complexity much smaller set of prototypes during training. A pret decisions. also results in easier-to-inter- We empirically show that our approach can reduce up to ten times the complexity of Support Vector Machines, LASSO and ridge regression almost affecting at test time, without their classification accuracy. |
KEBIJAKAN LUAR NEGERI AMERIKA SERIKAT DI BAWAH PRESIDEN REAGAN
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-2, PEBRUARI (No. 2) |
| Halaman | : | 83-94 |
| Abstrak | : | - |
Regularized Multivariate Analysis Framework for Interpretable High-Dimensional Variable Selection
| Pengarang | : | Muñoz-Romero, Sergio,Gómez-Verdejo, Vanessa,Arenas-Garcia, Jerónimo |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 4) |
| Halaman | : | 24-35 |
| Abstrak | : | Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction which exploit correlations among input variables representing the data. One important property that is enjoyed by most such methods is uncorrelation among the extracted features. Recently, regularized versions of MVA methods have appeared in the literature, mainly with the goal to gain interpretability of the solution. In these cases, the solutions can no longer be obtained in a closed manner, and more complex optimization methods that rely on the iteration of two steps are frequently used. This paper recurs to an alternative approach to solve efficiently this iterative problem. The main novelty of this approach lies in preserving several properties of the original methods, most notably the uncorrelation of the extracted features. Under this framework, we proposea novel method that takes advantage of the 2,1 norm to perform variable selection during the feature extraction process. Experimental results over different problems corroborate the advantages of the proposed formulation in comparison to state of the art formulations. |
Learning Distributional Parameters for Adaptive Bayesian Sparse Signal Recovery
| Pengarang | : | Giri, Ritwik,Rao, Bhaskar |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 4) |
| Halaman | : | 14-23 |
| Abstrak | : | Power Exponential Scale Mixture (PESM), a generalized scale mixture family of distributions, has been recently proposed to model the sparsity inducing prior distributions currently in use for Sparse Signal Recovery (SSR). In this paper, we review this generalized scale mixture family and establish the necessary and sufficient condition for a distribution (symmetric with respect to origin) to have a PESM representation, which is a generalization of the results previously known for the Gaussian Scale Mixture (GSM) family. On the algorithmic front, we propose an adaptive Bayesian Sparse Signal Recovery (B-SSR) framework by learning the distributional parameters of a Generalized t-distribution (GT) which belongs to the PESM family. For specific choice of distributional parameters of GT our proposed framework corresponds to popular sparse recovery algorithms such as, LASSO, Reweighted 1 norm minimization, Reweighted l2 norm minimization etc. The tail nature of GT distribution family is extensively studied in this paper and an adaptive algorithm has been proposed where the tail nature of the prior is adapted over iterations based on the observation. Extensive experimental results based on traditional SSR setup have also been presented to show the efficacy of this adaptive approach. |
Model Complexity, Regularization, and Sparsity
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 4) |
| Halaman | : | 12-13 |
| Abstrak | : | - |
CIS Publication Spotlight
| Pengarang | : | Haibo He,Chin-Teng Lin,Kay Chen Tan,Kendall, Graham,Yaochu Jin |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 11 (No. 4) |
| Halaman | : | 8-10 |
| Abstrak | : | - |