Design and Analysis of Ternary m-sequences with Interleaved Structure by d-Transform

Multilevel sequences find more and more applications in modern modulation schemes [4QPSK, 8QPSK,16QAM..]  for the 3G ,4G system air interface [1,2].Furthermore, in modern cryptography they are also widerly used. It is also interesting to point out that the length L of these sequences are composite numbers( L=NS),that means the sequence can be easily implemented by interleaving S subsequences, each of length S.Therefore, the methods to develop multilevel sequence with interleaved structure draw a lot of attentions [3, 4]. In this contribution, a method for design and analysis of ternary m-sequences with interleaved structure is presented, based on the d-transform, Which turns out to be a very effective and versal tool for this purpose. Simulations have been made to verify the theory. We first introduce d-transform and its properties and then work out the procedure to design an interleaving sequence in d-transform. Keywords: d-transform,q-ary sequences, interleaved sequence

Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition

In this work, we investigate the problem of face reconstruction given a facial feature representation extracted from a blackbox face recognition engine. Indeed, it is a very challenging problem in practice due to the limitations of abstracted information from the engine. We, therefore, introduce a new method named Attention-based Bijective Generative Adversarial Networks in a Distillation framework (DAB-GAN) to synthesize the faces of a subject given his/her extracted face recognition features. Given any unconstrained unseen facial features of a subject, the DAB-GAN can reconstruct his/her facial images in high definition. The DAB-GAN method includes a novel attention-based generative structure with the newly defined Bijective Metrics Learning approach. The framework starts by introducing a bijective metric so that the distance measurement and metric learning process can be directly adopted in the image domain for an image reconstruction task. The information from the blackbox face recognition engine will be optimally exploited using the global distillation process. Then an attention-based generator is presented for a highly robust generator to synthesize realistic faces with ID preservation. We have evaluated our method on the challenging face recognition databases, i.e., CelebA, LFW, CFP-FP, CP-LFW, AgeDB, CA-LFW, and consistently achieved state-of-the-art results. The advancement of DAB-GAN is also proven in both image realism and ID preservation properties.Comment: arXiv admin note: substantial text overlap with arXiv:2003.0695

Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models

Recognition across domains has recently become an active topic in the research community. However, it has been largely overlooked in the problem of recognition in new unseen domains. Under this condition, the delivered deep network models are unable to be updated, adapted or fine-tuned. Therefore, recent deep learning techniques, such as: domain adaptation, feature transferring, and fine-tuning, cannot be applied. This paper presents a novel Universal Non-volume Preserving approach to the problem of domain generalization in the context of deep learning. The proposed method can be easily incorporated with any other ConvNet framework within an end-to-end deep network design to improve the performance. On digit recognition, we benchmark on four popular digit recognition databases, i.e. MNIST, USPS, SVHN and MNIST-M. The proposed method is also experimented on face recognition on Extended Yale-B, CMU-PIE and CMU-MPIE databases and compared against other the state-of-the-art methods. In the problem of pedestrian detection, we empirically observe that the proposed method learns models that improve performance across a priori unknown data distributions

Organisational Baseline Study: Overview report for Tra Hat CSV, Vietnam (VN03)

This report covers the Organisational Baseline Study (OBS) for the CCAFS Climate-Smart Village Tra Hat in the South Viet Nam. During October 2014 interviews were conducted with local stakeholders at ten organisations who are working or collaborating with farmers and/or the community in Vinh Loi district, Bac Lieu province. The Tra Hat CSV is located near the coastal area, at tail end of a primary canal of Quan Lo Phung Hiep system (QLPH), the Mekong Delta of Vietnam, it usually causes lack of fresh water in from QLPH in dry season. There are two distinct dry season (December to April) and rainy season (May to November) which typhoon happens seldom in rainy season. Protected by dyke and sluice system of QLPH in Bac Lieu province, Tra Hat has not been affected by saline intrusion for last 15 years. The main farming systems in the village comprise two or three rice crops per year, small livestock as pig, chicken and ducks. Besides, mixed fruit garden and cash crop are often blended in residential area. Ground water and water in ponds is popular in household to provide domestic water, raising fish or garden irrigation and livestock, especially in dry season. The objectives of the OBS study are to: Provide indicators to monitor changes in behaviours and practices of locally relevant organisations that have climate change related activities in Bac Lieu over time Understand the provision of information/services at the local level that informs farmers’ decision making about their livelihood strategies in response to climate change This OBS report also supplements to the quantitative Household Baseline Survey (HBS) and the qualitative Village Baseline Studies (VBS) in Tra Hat CSV and surrounding villages

Village Baseline Study: Site Analysis Report for Tra Hat Village, Vinh Loi, Bac Lieu, Viet Nam (VNM 03)

This report presents data collected from the Village Baseline Study conducted on 2-4 October 2014 at the Tra Hat village, Vinh Loi district, Bac Lieu, Vietnam. Data were collected through focus group discussions and participatory resource mapping with community members in the village. The Village Baseline Study is part of the baseline activities conducted under the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) in South East Asia. The purpose is to collect data for indicators that will allow site comparability and monitoring to assess changes in terms of food security and natural resource management across time. Results show that the men and women in Tra Hat village consider farmland, rivers and canals as important natural resources. The quality, however, of land, water and wildlife habitats has declined in the last decade along with the improvement of farming techniques and intensive use of chemical fertilizers and pesticides. Infrastructures such as roads, internal canals, hospitals, schools, water supply station and electricity transformer station have improved. The future is envisioned to have improved internal canals in farmlands and a developed irrigation system, dykes and sluices to support high agriculture production. Home garden diversification was also believed to enhance food security and improve livelihood resilience. To turn the vision into reality, the community expects support from the different organizations working in the area considering current impacts of salinity intrusion and sea level rise, the need interventions of CCAFS and its partners. Strengthening the irrigation system, improving local rice variety, and introducing modern farming techniques taking into account negative impacts of climate change are major recommendation for future intervention