Kung Yao

Bio: Kung Yao is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Beamforming & Systolic array. The author has an hindex of 33, co-authored 151 publications receiving 4961 citations.

Maximum-likelihood source localization and unknown sensor location estimation for wideband signals in the near-field

Abstract: In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.

Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus

Abstract: Summary 1. Animals produce sounds for diverse biological functions such as defending territories, attracting mates, deterring predators, navigation, finding food and maintaining contact with members of their social group. Biologists can take advantage of these acoustic behaviours to gain valuable insights into the spatial and temporal scales over which individuals and populations interact. Advances in bioacoustic technology, including the development of autonomous cabled and wireless recording arrays, permit data collection at multiple locations over time. These systems are transforming the way we study individuals and populations of animals and are leading to significant advances in our understandings of the complex interactions between animals and their habitats. 2. Here, we review questions that can be addressed using bioacoustic approaches, by providing a primer on technologies and approaches used to study animals at multiple organizational levels by ecologists, behaviourists and conservation biologists. 3. Spatially dispersed groups of microphones (arrays) enable users to study signal directionality on a small scale or to locate animals and track their movements on a larger scale. 4. Advances in algorithm development can allow users to discriminate among species, sexes, age groups and individuals. 5. With such technology, users can remotely and non-invasively survey populations, describe the soundscape, quantify anthropogenic noise, study species interactions, gain new insights into the social dynamics of sound-producing animals and track the effects of factors such as climate change and habitat fragmentation on phenology and biodiversity. 6. There remain many challenges in the use of acoustic monitoring, including the difficulties in performing signal recognition across taxa. The bioacoustics community should focus on developing a

A representation theorem and its applications to spherically-invariant random processes

Abstract: The n th-order characteristic functions (cf) of spherically-invariant random processes (sirp) with zero means are defined as cf, which are functions of n th-order quadratic forms of arbitrary positive definite matrices p . Every n th-order spherically-invariant characteristic function (sicf) is represented as a weighted Lebesgue-Stieltjes integral transform of an arbitrary univariate probability distribution function F(\cdot) on [0,\infty) . Furthermore, every n th-order sicf has a corresponding spherically-invariant probability density (sipd). Then we show that every n th-order sicf (or sipd) is a random mixture of a n th-order Gaussian cf [or probability density]. The randomization is performed on u^2 \rho , where u is a random variable (tv) specified by the F(\cdot) function. Examples of sirp are given. Relations to previously known results are discussed. Various expectation properties of Gaussian random processes are valid for sirp. Related conditional expectation, mean-square estimation, semMndependence, martingale, and closure properties are given. Finally, the form of the unit threshold likelihood ratio receiver in the detection of a known deterministic signal in additive sirp noise is shown to be a correlation receiver or a matched filter. The associated false-alarm and detection probabilities are expressed in closed forms.

Blind beamforming on a randomly distributed sensor array system

Abstract: We consider a digital signal processing sensor array system, based on randomly distributed sensor nodes, for surveillance and source localization applications. In most array processing the sensor array geometry is fixed and known and the steering array vector/manifold information is used in beamformation. In this system, array calibration may be impractical due to unknown placement and orientation of the sensors with unknown frequency/spatial responses. This paper proposes a blind beamforming technique, using only the measured sensor data, to form either a sample data or a sample correlation matrix. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generalization of Szego's (1958) theory of the asymptotic distribution of eigenvalues of the Toeplitz form. An efficient blind beamforming time delay estimate of the dominant source is proposed. Source localization based on a least squares (LS) method for time delay estimation is also given. Results based on analysis, simulation, and measured acoustical sensor data show the effectiveness of this beamforming technique for signal enhancement and space-time filtering.

Accumulate-Repeat-Accumulate Codes

Abstract: In this paper, we propose an innovative channel coding scheme called accumulate-repeat-accumulate (ARA) codes. This class of codes can be viewed as serial turbo-like codes or as a subclass of low-density parity check (LDPC) codes, and they have a projected graph or protograph representation; this allows for high-speed iterative decoding implementation using belief propagation. An ARA code can be viewed as precoded repeat accumulate (RA) code with puncturing or as precoded irregular repeat accumulate (IRA) code, where simply an accumulator is chosen as the precoder. The amount of performance improvement due to the precoder will be called precoding gain. Using density evolution on their associated protographs, we find some rate-1/2 ARA codes, with a maximum variable node degree of 5 for which a minimum bit SNR as low as 0.08 dB from channel capacity threshold is achieved as the block size goes to infinity. Such a low threshold cannot be achieved by RA, IRA, or unstructured irregular LDPC codes with the same constraint on the maximum variable node degree. Furthermore, by puncturing the inner accumulator, we can construct families of higher rate ARA codes with thresholds that stay close to their respective channel capacity thresholds uniformly. Iterative decoding simulation results are provided and compared with turbo codes. In addition to iterative decoding analysis, we analyzed the performance of ARA codes with maximum-likelihood (ML) decoding. By obtaining the weight distribution of these codes and through existing tightest bounds we have shown that the ML SNR threshold of ARA codes also approaches very closely to that of random codes. These codes have better interleaving gain than turbo codes

Energy-efficient communication protocol for wireless microsensor networks

Abstract: Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

Energy-efficient communication protocols for wireless microsensor networks

Abstract: Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have signicant impact on the overall energy dissipation of these networks. Based on our ndings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.

An application-specific protocol architecture for wireless microsensor networks

Abstract: Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.

Wireless integrated network sensors

Abstract: W ireless integrated network sensors (WINS) provide distributed network and Internet access to sensors, controls, and processors deeply embedded in equipment, facilities, and the environment. The WINS network represents a new monitoring and control capability for applications in such industries as transportation, manufacturing, health care, environmental oversight, and safety and security. WINS combine microsensor technology and low-power signal processing, computation, and low-cost wireless networking in a compact system. Recent advances in integrated circuit technology have enabled construction of far more capable yet inexpensive sensors, radios, and processors, allowing mass production of sophisticated systems linking the physical world to digital data networks [2–5]. Scales range from local to global for applications in medicine, security, factory automation, environmental monitoring, and condition-based maintenance. Compact geometry and low cost allow WINS to be embedded and distributed at a fraction of the cost of conventional wireline sensor and actuator systems. WINS opportunities depend on development of a scalable, low-cost, sensor-network architecture. Such applications require delivery of sensor information to the user at a low bit rate through low-power transceivers. Continuous sensor signal processing enables the constant monitoring of events in an environment in which short message packets would suffice. Future applications of distributed embedded processors and sensors will require vast numbers of devices. Conventional methods of sensor networking represent an impractical demand on cable installation and network bandwidth. Processing at the source would drastically reduce the financial, computational, and management burden on communication system

On the capacity of a cellular CDMA system

Abstract: It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >