Optimizing a transmit MIMO radar waveform subject to the non-convex constant modulus constraint remains a problem of enduring interest. The past decade has seen a variety of tailored iterative approaches with various performance-complexity trade-offs. Despite promising work, iterative algorithms have a speed handicap and require meticulous parameter tuning. Once trained, a deep network can quickly regress the desired waveform coefficients, but it is a black box and may excel only when generous training is available. We present a fast, learned, and - for the first time - interpretable (FLI) deep learning approach by unrolling a state-of-the-art iterative optimization approach. We particularly leverage the recently proposed projection, descent, and retraction (PDR) algorithm and design a deep network where each PDR step is mapped to a layer in the neural network while preserving the non-convex constant modulus constraint. FLI breaks the trade-off between complexity and performance. It is near real-time with boosted performance – fidelity to the desired beampattern – compared to the state-of-the-art alternatives.

Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formulated as a multi-label classification problem. Yet significant challenges remain in: 1) predicting a large number of attributes over multiple object categories, 2) modeling category-dependence of attributes, 3) methodically capturing both global and local scene context, and 4) robustly predicting attributes of objects with low pixel-count. To address these issues, we propose a novel multi-category attribute prediction deep architecture named GlideNet, which contains three distinct feature extractors. A global feature extractor recognizes what objects are present in a scene, whereas a local one focuses on the area surrounding the object of interest. Meanwhile, an intrinsic feature extractor uses an extension of standard convolution dubbed Informed Convolution to retrieve features of objects with low pixel-count utilizing its binary mask. GlideNet then uses gating mechanisms with binary masks and its self-learned category embedding to combine the dense embeddings. Collectively, the Global-Local-Intrinsic blocks comprehend the scene's global context while attending to the characteristics of the local object of interest. The architecture adapts the feature composition based on the category via category embedding. Finally, using the combined features, an interpreter predicts the attributes, and the length of the output is determined by the category, thereby removing unnecessary attributes. GlideNet can achieve compelling results on two recent and challenging datasets - VAW and CAR -for large-scale attribute prediction. For instance, it obtains more than 5% gain over state of the art in the mean recall (mR) metric. GlideNet's advantages are especially apparent when predicting attributes of objects with low pixel counts as well as attributes that demand global context understanding. Finally, we show that GlideNet excels in training starved real-world scenarios.

High Dynamic Range (HDR) reconstruction is the process of producing an HDR image from a set of Standard Dynamic Range (SDR) images with different exposure times. This is a particularly challenging problem when relative camera or object motion exists between the available SDR images. Recently, deep learning methods, specifically those based on convolutional neural networks (CNNs) have been developed for HDR and shown to achieve unprecedented quality gains. Invariably an image alignment phase precedes the CNN mapping and merging. In practice, this alignment step greatly increases the computational burden of deep HDR methods often rendering them unsuitable for real-time composition. We propose a new deep HDR technique that does not need any explicit alignment of SDR images. Instead, a novel attention mask is developed that enables the network to focus on parts of the scene with considerable motion. Further, a dense merger is proposed that leads to an economical network. Evaluation over benchmark databases reveals that the proposed AttenDense network achieves high quality HDR results with significantly reduced computation time than state of the art. Further, the incorporation of domain knowledge (development of a custom attention mask) allows a more graceful decay in performance in the face of limited training.

Due to the increasing demand for higher data rates and the congestion in communication systems, new research is focusing on the cooperation between the two most successful communication systems, LTE and WiFi. The overall performance of a WiFi system degrades with increasing the number of served users due to collisions. We propose in this paper a novel scheme for LTE and WiFi coexistence, where an LTE femto Base Station cooperates with a WiFi Access Point to maximize both of their profits. Our proposed scheme has the advantage of relieving a congested WiFi system. Thus, this creates a time gap for the LTE system to transmit its data. In addition, we investigate the capability of WiFi and LTE systems to work simultaneously under a certain maximum interference limit. We have formulated a multi- objective optimization problem for maximizing the rate of the WiFi system and the capacity of the LTE system. We developed an algorithm based on particle swarm optimization to determine the appropriate time ratios for WiFi and LTE transmission, the transmitting power of LTE under WiFi transmission, and the number of WiFi nodes to be transferred to LTE system. Simulation results confirm the capability for LTE to transmit besides WiFi without affecting its transmission rate.

Analog network coding has been successfully employed in conventional two way relaying systems. However, analog two way relaying is successful when the two sender and destination nodes are symmetric. In practical cellular-based mobile communication systems of the likes of Long Term Evolution (LTE), the system is composed of several powerful base-stations and low power users' equipment. Applying classical two way relaying will cause unbearable interference. In this paper we propose applying network coding between two user equipment in the same cell in the uplink direction. We investigate Single Input Single Output (SISO) and Multiple Input Multiple Output (MIMO) models for this scenario. We also study the effect of interference of other nodes in surrounding cells.