Impossible differential cryptanalysis of 7-round Advanced Encryption Standard (AES)

In 2000, Biham and Keller [Cryptanalysis of reduced variants of Rijndael, 3rd AES Conference, in press] presented an impossible differential cryptanalysis of the Advanced Encryption Standard (AES) up to 5 rounds. This was later improved in 2001 by Cheon et al. [Improved impossible differential cryptanalysis of Rijndael and Crypton, in: Lecture Notes in Comput. Sci., vol.  2288, Springer-Verlag, Berlin, 2001, pp. 39-49] to apply to 6 rounds of the AES. In this paper, we extend on previous results to present an attack on the AES up to 7 rounds. This is the best-known impossible differential attack on the AES, and works by exploiting weaknesses in the AES key schedule.     

Seamless handover supported by parallel polling and dynamic multicast group in connected WLAN micro-cells system

WLAN (Wireless Local Area Network) has been seen to be one of the promising access technologies that adapts to 4G cellular network systems in providing very high speed connection with QoS guarantee through the polling function. However, when the handover happens, the contention-based medium access mechanism which is mainly used in WLAN is invoked and introduces unbounded transmission delay due to idle time periods and retransmission because of collision during the handover. If this technique is expanded to use in a microcellular network such as connected WLAN micro-cells, contention-based mechanism, therefore, should not be used to handle the MT’s handover, especially for vehicular users who change access point every few seconds. To overcome these difficulties in handover, we introduce parallel polling scheme in dynamic LMC (Logical Macro Cell) which can reduce delays much and remove packet loss rate. LMC is a virtual single macro cell which is built on a multicast group of adjacent micro-cells. In the same LMC, polling signals are sent from every BS (base station) to give MT (mobile terminal) permission to access one of these BSs. Instead of wasting much time to contend for resources of a new BS during handover, the MT answers the polling as an acknowledgment to connect to that new BS. The polling response is controlled to multicast to all BSs of the same LMC via the core network to synchronize for the next polling cycle. LMC is controlled to dynamically change when the MT comes in a new BS to make polling signals be continuous in a new LMC. Through analytical and simulation results, we show that the parallel polling scheme can achieve no handover latency, no packet loss and maintain mobile users’ throughput stably in the high traffic load condition though it causes overhead on the neighboring cells in both of wired and wireless sections. At speeds of up to70 m/s, the MT can still maintain its stable connection. OMNeT++ simulator with INET project is used to evaluate our proposal.     

Scheduling Optimization Modelling: A Case Study of a Woven Label Manufacturing Company

Production scheduling involves all activities of building production schedules, including coordinating and assigning activities to each person, group of people, or machine and arranging work orders in each workplace. Production scheduling must solve all problems such as minimizing customer wait time, storage costs, and production time; and effectively using the enterprise’s human resources. This paper studies the application of flexible job shop modelling on scheduling a woven labelling process. The labelling process includes several steps which are handled in different work-stations. Each workstation is also comprised of several identical parallel machines. In this study, job splitting is allowed so that the power of work stations can be utilized better. The final objective is to minimize the total completion time of all jobs. The results show a significant improvement since the new planning may save more than 60% of lead time compared to the current schedule. The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process. The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries. A practical case study demonstrates the effectiveness of the proposed model.     

Cryptanalysis of a New Ultralightweight RFID Authentication Protocol—SASI

Since RFID tags are ubiquitous and at times even oblivious to the human user, all modern RFID protocols are designed to resist tracking so that the location privacy of the human RFID user is not violated. Another design criterion for RFIDs is the low computational effort required for tags, in view that most tags are passive devices that derive power from an RFID reader's signals. Along this vein, a class of ultralightweight RFID authentication protocols has been designed, which uses only the most basic bitwise and arithmetic operations like exclusive-OR, OR, addition, rotation, and so forth. In this paper, we analyze the security of the SASI protocol, a recently proposed ultralightweight RFID protocol with better claimed security than earlier protocols. We show that SASI does not achieve resistance to tracking, which is one of its design objectives.     

Negation as failure for disjunctive logic programming

We generalize the Negation-as-Failure procedure for disjunctive logic programming. Then we compare our method with related methods in the literature. We also propose a new completion theory for disjunctive logic programs which allows some program clauses to have inclusive meaning and the others exclusive meaning.     

Optimal bilinear observers for bilinear state-space models by interaction matrices

This paper formulates optimal bilinear observers for bilinear state-space models. Observers in bilinear form, as opposed to other nonlinear forms, are required to develop an extension of observer/Kalman filter identification for simultaneous identification of a bilinear state-space model and an associated bilinear observer from noisy input–output measurements. The paper establishes the relationship between the bilinear observer gains and the interaction matrices which are used to convert the original bilinear state-space model to a form that simplifies the identification of such a model. Techniques to find the interaction matrices are developed. In the absence of noises, these matrices produce the gains of the fastest converging observer. In the presence of noises, they minimise the state estimation error in the same manner as a standard steady-state Kalman filter. Numerical examples illustrate both the theoretical and computational aspects of the proposed algorithms.     

The Global Exponential Stability of a Dynamical System for Solving Variational Inequalities

Networks and Spatial Economics - We revisit a dynamical system for solving variational inequalities. Under strongly pseudomonotone and Lipschitz continuous assumptions of the considered operator,...     

Investigation of long term drift of an intraocular pressure sensor

Microsystem Technologies - “Zero drift” behavior of an optical intraocular pressure sensor is studied using an analytical model based on the deflection of a circular membrane. Results...     

Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning. It influences activities such as purchasing, manufacturing, and selling, and quick adaption is required. In production planning, for reasons such as reducing costs and obtaining supplier discounts, many decisions must be made in the initial stage when demand has not been realized. The effects of non-optimal decisions will propagate to later stages, which can lead to losses due to overstocks or out-of-stocks. To find the optimal solutions for the initial and later stage regarding demand realization, this study proposes a stochastic two-stage linear programming model for a multi-supplier, multi-material, and multi-product purchasing and production planning process. The objective function is the expected total cost after two stages, and the results include detailed plans for purchasing and production in each demand scenario. Small-scale problems are solved through a deterministic equivalent transformation technique. To solve the problems in the large scale, an algorithm combining metaheuristic and sample average approximation is suggested. This algorithm can be implemented in parallel to utilize the power of the solver. The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given, then the problems of the first and second stages can be decomposed.